Scope of Analysis

• How the daily positive cases changes over time ?
• What has the vaccination coverage and its progression been?
• Analyzing hospitalization and fatality trends
• Projection of hospitalization and deaths in near future

Daily Cases:

• There has been a total of 571,421 Positive cases reported as of 30th September in Scotland.
• Between 23 September 2021 and 30 September 2021, 21,279 people had a confirmed positive test result.
  This shows a decrease of 9.66% compared to the previous 7 days.

Vaccination

As of 30th September in Scotland,

• 4,195,393 people had been given first dose (91.43%)
• 3,842,053 people had been given second dose (81.45%)

COVID-19 Hospitalizations and Deaths

COVID-19 Hospitalization Projection: ARIMA Model(p,d,q):

The Automated Regression Integrated Moving Average technique is used for the COVID-19 Time Series Data.

• p : order of Auto Regression
• d : degree of Difference (Integrated)
• q : order of the Moving Average.

Based on statistical parameters (lower AIC, RMSE and p-value), an ARIMA (3,1,2) model has been chosen for Hospital projections.

COVID-19 Deaths Projection ARIMA Model(p,d,q):

Based on statistical parameters (lower AIC, RMSE and p-value)
ARIMA(2,1,3) with lag = 1 has been chosen.

Key Points

Summary:

• Daily Cases trend seems to be declining as we see a decrease of 9.66% when compared to previous 7 days.
• Almost 91.43% of the people are vaccinated with Dose 1 and 84.45% of the people are vaccinated with Dose2.

Projections:

• Hospitalization shows a decrease of 15.11% when compared to previous 7 days.
• The ARIMA model that we built projects that the hospitalizations might drop, based on the declining trend of the current COVID-19 Hospitalizations,
• The ARIMA model also projects that the weekly deaths maintains at the same level.
• However potential future changes in hospital occupancy and intensive care use is highly uncertain as it is also dependent on both current infection levels and how the vaccination impact will be.

Future Proposal:

• Even though the trends on daily positive cases, hospitalizations and death seems to be declining in Scotland, England shows an increase in all the categories.
• Given that the border is fully open, this model could be expanded to include data from the whole of UK which might give a different perspective.
• As a preventive measure, the travel restriction might help to reduce the spread of infection and control the hospitalization.
• With winter approaching, considering the previous trend, its better for the hospitals to be prepared.

Thank you !!!

Questions !!!

Appendix

Auto Regression Integrated Moving Average: ARIMA (p, d, q) Model

2. Augmented Dicky Fuller Test (Finding d)

##            Data Dickey_Fuller   p_value
## 1      Original     -1.088300 0.9208883
## 2 First-Ordered     -7.507224 0.0100000

3. Estimate the Parameters (Finding p and q)

Auto Regression Integrated Moving Average: ARIMA (p, d, q) Model (contd..)

4. Build the ARIMA model

## 
## ========================================================
##                 ARIMA(3,1,2)  ARIMA(3,1,0)  ARIMA(0,1,2)
## --------------------------------------------------------
## ar1                0.92 ***     -0.26 **                
##                   (0.09)        (0.09)                  
## ar2               -0.58 ***     -0.23 *                 
##                   (0.11)        (0.09)                  
## ar3               -0.31 ***     -0.16                   
##                   (0.09)        (0.09)                  
## ma1               -1.35 ***                   -0.28 **  
##                   (0.03)                      (0.09)    
## ma2                1.00 ***                   -0.17 *   
##                   (0.04)                      (0.08)    
## --------------------------------------------------------
## AIC             1002.61       1027.69       1025.20     
## AICc            1003.35       1028.03       1025.40     
## BIC             1019.39       1038.87       1033.58     
## Log Likelihood  -495.31       -509.84       -509.60     
## Num. obs.        121           121           121        
## ========================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05

## 
## ============================
##                 ARIMA(3,1,2)
## ----------------------------
## ar1                0.94 *** 
##                   (0.10)    
## ar2               -0.56 *** 
##                   (0.11)    
## ar3               -0.25 *   
##                   (0.10)    
## ma1               -1.44 *** 
##                   (0.05)    
## ma2                0.94 *** 
##                   (0.04)    
## ----------------------------
## AIC              983.97     
## AICc             984.71     
## BIC             1000.70     
## Log Likelihood  -485.99     
## Num. obs.        120        
## ============================
## *** p < 0.001; ** p < 0.01; * p < 0.05

Auto Regression Integrated Moving Average: ARIMA (p, d, q) Model (contd..)

5. Check the diagnostics

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(3,1,2)
## Q* = 33.437, df = 5, p-value = 3.082e-06
## 
## Model df: 5.   Total lags used: 10

Auto Regression Integrated Moving Average: ARIMA (p, d, q) Model (contd..)

7. Hospitalization Projection using ARIMA (3,1,2)

Based on the (AIC),(RMSE), and (MAPE) , it’s been identified that the model ARIMA(3, 1, 2) with lag = 1 seems reasonable.